// Artificial Intelligence A Modern Approach (2nd Edition): page 288.
// COMPOSE(delta, tau) is the substitution whose effect is identical to
// the effect of applying each substitution in turn. That is,
// SUBST(COMPOSE(theta1, theta2), p) = SUBST(theta2, SUBST(theta1, p))
private IMap <Variable, Term> compose(FOLKnowledgeBase KB, IMap <Variable, Term> theta1, IMap <Variable, Term> theta2)
{
IMap <Variable, Term> composed = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
// So that it behaves like:
// SUBST(theta2, SUBST(theta1, p))
// There are two steps involved here.
// See: http://logic.stanford.edu/classes/cs157/2008/notes/chap09.pdf
// for a detailed discussion:
// 1. Apply theta2 to the range of theta1.
foreach (Variable v in theta1.GetKeys())
{
composed.Put(v, KB.subst(theta2, theta1.Get(v)));
}
// 2. Adjoin to delta all pairs from tau with different
// domain variables.
foreach (Variable v in theta2.GetKeys())
{
if (!theta1.ContainsKey(v))
{
composed.Put(v, theta2.Get(v));
}
}
return(cascadeSubstitutions(KB, composed));
}
public BidirectionalSearch(NodeExpander <S, A> nodeExpander)
: base(nodeExpander)
{
explored = CollectionFactory.CreateQueue <IMap <S, ExtendedNode> >();
explored.Add(CollectionFactory.CreateInsertionOrderedMap <S, ExtendedNode>());
explored.Add(CollectionFactory.CreateInsertionOrderedMap <S, ExtendedNode>());
}
private void sampleFromTransitionModel(int i)
{
// x <- an event initialized with S[i]
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
for (int n = 0; n < S[i].Length; n++)
{
AssignmentProposition x1 = S[i][n];
x.Put(this.dbn.GetX_1_to_X_0().Get(x1.getTermVariable()), x1.getValue());
}
// foreach variable X<sub>1<sub>i</sub></sub> in
// X<sub>1<sub>1</sub></sub>,...,X<sub>1<sub>n<</sub>/sub> do
foreach (IRandomVariable X1_i in dbn.GetX_1_VariablesInTopologicalOrder())
{
// x1[i] <- a random sample from
// <b>P</b>(X<sub>1<sub>i</sub></sub> |
// parents(X<sub>1<sub>i</sub></sub>))
x.Put(X1_i, ProbUtil.randomSample(dbn.GetNode(X1_i), x, randomizer));
}
// S[i] <- sample from <b>P</b>(<b>X</b><sub>1</sub> |
// <b>X</b><sub>0</sub> = S[i])
for (int n = 0; n < S_tp1[i].Length; n++)
{
AssignmentProposition x1 = S_tp1[i][n];
x1.setValue(x.Get(x1.getTermVariable()));
}
}
private static int standardizeApart(ICollection <Variable> variables, object expr, int saIdx)
{
IMap <string, int> indexicals = CollectionFactory.CreateInsertionOrderedMap <string, int>();
foreach (Variable v in variables)
{
if (!indexicals.ContainsKey(v.getIndexedValue()))
{
indexicals.Put(v.getIndexedValue(), saIdx++);
}
}
foreach (Variable v in variables)
{
if (!indexicals.ContainsKey(v.getIndexedValue()))
{
throw new RuntimeException("ERROR: duplicate var=" + v + ", expr=" + expr);
}
else
{
v.setIndexical(indexicals.Get(v.getIndexedValue()));
}
}
return(saIdx);
}
public DecisionList(string positive, string negative)
{
this.positive = positive;
this.negative = negative;
this.tests = CollectionFactory.CreateQueue <DecisionListTest>();
testOutcomes = CollectionFactory.CreateInsertionOrderedMap <DecisionListTest, string>();
}
public static T mode <T>(ICollection <T> l)
{
IMap <T, int> hash = CollectionFactory.CreateInsertionOrderedMap <T, int>();
foreach (T obj in l)
{
if (hash.ContainsKey(obj))
{
hash.Put(obj, hash.Get(obj) + 1);
}
else
{
hash.Put(obj, 1);
}
}
T maxkey = hash.GetKeys().Get(0);
foreach (T key in hash.GetKeys())
{
if (hash.Get(key) > hash.Get(maxkey))
{
maxkey = key;
}
}
return(maxkey);
}
// function WEIGHTED-SAMPLE(bn, e) returns an event and a weight
/**
* The WEIGHTED-SAMPLE function in Figure 14.15.
*
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @return return <b>x</b>, w - an event with its associated weight.
*/
public Pair <IMap <IRandomVariable, object>, double> weightedSample(IBayesianNetwork bn, AssignmentProposition[] e)
{
// w <- 1;
double w = 1.0;
// <b>x</b> <- an event with n elements initialized from e
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
foreach (AssignmentProposition ap in e)
{
x.Put(ap.getTermVariable(), ap.getValue());
}
// foreach variable X<sub>i</sub> in X<sub>1</sub>,...,X<sub>n</sub> do
foreach (IRandomVariable Xi in bn.GetVariablesInTopologicalOrder())
{
// if X<sub>i</sub> is an evidence variable with value x<sub>i</sub>
// in e
if (x.ContainsKey(Xi))
{
// then w <- w * P(X<sub>i</sub> = x<sub>i</sub> |
// parents(X<sub>i</sub>))
w *= bn.GetNode(Xi)
.GetCPD()
.GetValue(ProbUtil.getEventValuesForXiGivenParents(bn.GetNode(Xi), x));
}
else
{
// else <b>x</b>[i] <- a random sample from
// <b>P</b>(X<sub>i</sub> | parents(X<sub>i</sub>))
x.Put(Xi, ProbUtil.randomSample(bn.GetNode(Xi), x, randomizer));
}
}
// return <b>x</b>, w
return(new Pair <IMap <IRandomVariable, object>, double>(x, w));
}
/**
* Access a folder of .txt files containing wikipedia html source, and give
* back a hashtable of pages, which each page having it's correct inlink
* list and outlink list.
*
* @param folderPath
* @return a hashtable of Page objects, accessed by article name (which is a
* location for wikipedia: \wiki\*article name*)
*/
public static IMap <string, Page> loadPages(string folderPath)
{
IMap <string, Page> pageTable = CollectionFactory.CreateInsertionOrderedMap <string, Page>();
Page currPage;
string[] listOfFiles;
wlf = new WikiLinkFinder();
if (Directory.Exists(folderPath))
{
listOfFiles = Directory.GetFiles(folderPath);
}
else
{
return(null);
} // maybe should throw exception instead?
// Access each .txt file to create a new Page object for that file's
// article
for (int i = 0; i < listOfFiles.Length; ++i)
{
currPage = wikiPageFromFile(folderPath, new FileInfo(listOfFiles[i]));
pageTable.Put(currPage.getLocation(), currPage);
}
// now that all pages are loaded and their outlinks have been determined,
// we can determine a page's inlinks and then return the loaded table
return(pageTable = determineAllInlinks(pageTable));
} // end loadPages()
public InferenceResult ask(Sentence query)
{
// Want to standardize apart the query to ensure
// it does not clash with any of the sentences
// in the database
StandardizeApartResult saResult = _standardizeApart.standardizeApart(query, queryIndexical);
// Need to map the result variables (as they are standardized apart)
// to the original queries variables so that the caller can easily
// understand and use the returned set of substitutions
InferenceResult infResult = getInferenceProcedure().ask(this, saResult.getStandardized());
foreach (Proof p in infResult.getProofs())
{
IMap <Variable, Term> im = p.getAnswerBindings();
IMap <Variable, Term> em = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
foreach (Variable rev in saResult.getReverseSubstitution().GetKeys())
{
em.Put((Variable)saResult.getReverseSubstitution().Get(rev), im.Get(rev));
}
p.replaceAnswerBindings(em);
}
return(infResult);
}
public Clause standardizeApart(Clause clause, StandardizeApartIndexical standardizeApartIndexical)
{
ISet <Variable> toRename = variableCollector.collectAllVariables(clause);
IMap <Variable, Term> renameSubstitution = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
foreach (Variable var in toRename)
{
Variable v = null;
do
{
v = new Variable(standardizeApartIndexical.getPrefix() + standardizeApartIndexical.getNextIndex());
// Ensure the new variable name is not already
// accidentally used in the sentence
} while (toRename.Contains(v));
renameSubstitution.Put(var, v);
}
if (renameSubstitution.Size() > 0)
{
ICollection <Literal> literals = CollectionFactory.CreateQueue <Literal>();
foreach (Literal l in clause.getLiterals())
{
literals.Add(substVisitor.subst(renameSubstitution, l));
}
Clause renamed = new Clause(literals);
renamed.setProofStep(new ProofStepRenaming(renamed, clause.getProofStep()));
return(renamed);
}
return(clause);
}
public static Example exampleFromString(string data, DataSetSpecification dataSetSpec, string separator)
{
IRegularExpression splitter = TextFactory.CreateRegularExpression(separator);
IMap <string, IAttribute> attributes = CollectionFactory.CreateInsertionOrderedMap <string, IAttribute>();
ICollection <string> attributeValues = CollectionFactory.CreateQueue <string>(splitter.Split(data));
if (dataSetSpec.isValid(attributeValues))
{
ICollection <string> names = dataSetSpec.getAttributeNames();
int min = names.Size() > attributes.Size() ? names.Size() : attributes.Size();
for (int i = 0; i < min; ++i)
{
string name = names.Get(i);
IAttributeSpecification attributeSpec = dataSetSpec.getAttributeSpecFor(name);
IAttribute attribute = attributeSpec.CreateAttribute(attributeValues.Get(i));
attributes.Put(name, attribute);
}
string targetAttributeName = dataSetSpec.getTarget();
return(new Example(attributes, attributes.Get(targetAttributeName)));
}
else
{
throw new RuntimeException("Unable to construct Example from " + data);
}
}
public double getInformationFor()
{
string attributeName = specification.getTarget();
IMap <string, int> counts = CollectionFactory.CreateInsertionOrderedMap <string, int>();
foreach (Example e in examples)
{
string val = e.getAttributeValueAsString(attributeName);
if (counts.ContainsKey(val))
{
counts.Put(val, counts.Get(val) + 1);
}
else
{
counts.Put(val, 1);
}
}
double[] data = new double[counts.GetKeys().Size()];
int i = 0;
foreach (int value in counts.GetValues())
{
data[i] = value;
++i;
}
data = Util.normalize(data);
return(Util.information(data));
}
public object visitQuantifiedSentence(QuantifiedSentence sentence, object arg)
{
Sentence quantified = sentence.getQuantified();
ISet <Variable> universalScope = (Set <Variable>)arg;
// Skolemize: Skolemization is the process of removing existential
// quantifiers by elimination. This is done by introducing Skolem
// functions. The general rule is that the arguments of the Skolem
// function are all the universally quantified variables in whose
// scope the existential quantifier appears.
if (Quantifiers.isEXISTS(sentence.getQuantifier()))
{
IMap <Variable, Term> skolemSubst = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
foreach (Variable eVar in sentence.getVariables())
{
if (universalScope.Size() > 0)
{
// Replace with a Skolem Function
string skolemFunctionName = parser.getFOLDomain().addSkolemFunction();
ICollection <Term> terms = CollectionFactory.CreateQueue <Term>();
foreach (Variable v in universalScope)
{
terms.Add(v);
}
skolemSubst.Put(eVar, new Function(skolemFunctionName, terms));
}
else
{
// Replace with a Skolem Constant
string skolemConstantName = parser.getFOLDomain().addSkolemConstant();
skolemSubst.Put(eVar, new Constant(skolemConstantName));
}
}
Sentence skolemized = substVisitor.subst(skolemSubst, quantified);
return(skolemized.accept(this, arg));
}
// Drop universal quantifiers.
if (Quantifiers.isFORALL(sentence.getQuantifier()))
{
// Add to the universal scope so that
// existential skolemization may be done correctly
universalScope.AddAll(sentence.getVariables());
Sentence droppedUniversal = (Sentence)quantified.accept(this, arg);
// Enusre my scope is removed before moving back up
// the call stack when returning
universalScope.RemoveAll(sentence.getVariables());
return(droppedUniversal);
}
// Should not reach here as have already
// handled the two quantifiers.
throw new IllegalStateException("Unhandled Quantifier:" + sentence.getQuantifier());
}
public static IMap <IRandomVariable, IRandomVariable> getUmbrellaWorld_Xt_to_Xtm1_Map()
{
IMap <IRandomVariable, IRandomVariable> tToTm1StateVarMap = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, IRandomVariable>();
tToTm1StateVarMap.Put(ExampleRV.RAIN_t_RV, ExampleRV.RAIN_tm1_RV);
return(tToTm1StateVarMap);
}
public override void reset()
{
U = CollectionFactory.CreateInsertionOrderedMap <S, double>();
Ns.clear();
s = default(S);
a = default(A);
r = null;
}
/**
* Creates a new CSP.
*/
public CSP()
{
variables = CollectionFactory.CreateQueue <VAR>();
domains = CollectionFactory.CreateQueue <Domain <VAL> >();
constraints = CollectionFactory.CreateQueue <IConstraint <VAR, VAL> >();
varIndexHash = CollectionFactory.CreateInsertionOrderedMap <Variable, int>();
cnet = CollectionFactory.CreateInsertionOrderedMap <Variable, ICollection <IConstraint <VAR, VAL> > >();
}
public override void reset()
{
P.Clear();
R.Clear();
U = CollectionFactory.CreateInsertionOrderedMap <S, double>();
Nsa.clear();
NsDelta_sa.clear();
s = default(S);
a = default(A);
}
/// <summary>
/// Constructs a Table with the specified row and column headers.
/// </summary>
/// <param name="rowHeaders">a list of row headers</param>
/// <param name="columnHeaders">a list of column headers</param>
public Table(ICollection <RowHeaderType> rowHeaders, ICollection <ColumnHeaderType> columnHeaders)
{
this.rowHeaders = rowHeaders;
this.columnHeaders = columnHeaders;
this.rows = CollectionFactory.CreateInsertionOrderedMap <RowHeaderType, IMap <ColumnHeaderType, ValueType> >();
foreach (RowHeaderType rowHeader in rowHeaders)
{
rows.Put(rowHeader, CollectionFactory.CreateInsertionOrderedMap <ColumnHeaderType, ValueType>());
}
}
// function GIBBS-ASK(X, e, bn, N) returns an estimate of <b>P</b>(X|e)
/**
* The GIBBS-ASK algorithm in Figure 14.16. For answering queries given
* evidence in a Bayesian Network.
*
* @param X
* the query variables
* @param e
* observed values for variables E
* @param bn
* a Bayesian network specifying joint distribution
* <b>P</b>(X<sub>1</sub>,...,X<sub>n</sub>)
* @param Nsamples
* the total number of samples to be generated
* @return an estimate of <b>P</b>(X|e)
*/
public ICategoricalDistribution gibbsAsk(IRandomVariable[] X,
AssignmentProposition[] e, IBayesianNetwork bn, int Nsamples)
{
// local variables: <b>N</b>, a vector of counts for each value of X,
// initially zero
double[] N = new double[ProbUtil.expectedSizeOfCategoricalDistribution(X)];
// Z, the nonevidence variables in bn
ISet <IRandomVariable> Z = CollectionFactory.CreateSet <IRandomVariable>(bn.GetVariablesInTopologicalOrder());
foreach (AssignmentProposition ap in e)
{
Z.Remove(ap.getTermVariable());
}
// <b>x</b>, the current state of the network, initially copied from e
IMap <IRandomVariable, object> x = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
foreach (AssignmentProposition ap in e)
{
x.Put(ap.getTermVariable(), ap.getValue());
}
// initialize <b>x</b> with random values for the variables in Z
foreach (IRandomVariable Zi in Z)
{
x.Put(Zi, ProbUtil.randomSample(bn.GetNode(Zi), x, randomizer));
}
// for j = 1 to N do
for (int j = 0; j < Nsamples; j++)
{
// for each Z<sub>i</sub> in Z do
foreach (IRandomVariable Zi in Z)
{
// set the value of Z<sub>i</sub> in <b>x</b> by sampling from
// <b>P</b>(Z<sub>i</sub>|mb(Z<sub>i</sub>))
x.Put(Zi, ProbUtil.mbRandomSample(bn.GetNode(Zi), x, randomizer));
}
// Note: moving this outside the previous for loop,
// as described in fig 14.6, as will only work
// correctly in the case of a single query variable X.
// However, when multiple query variables, rare events
// will get weighted incorrectly if done above. In case
// of single variable this does not happen as each possible
// value gets * |Z| above, ending up with the same ratios
// when normalized (i.e. its still more efficient to place
// outside the loop).
//
// <b>N</b>[x] <- <b>N</b>[x] + 1
// where x is the value of X in <b>x</b>
N[ProbUtil.indexOf(X, x)] += 1.0;
}
// return NORMALIZE(<b>N</b>)
return(new ProbabilityTable(N, X).normalize());
}
/**
* Create a IMap<K, V> with the passed in keys having their values
* initialized to the passed in value.
*
* @param keys
* the keys for the newly constructed map.
* @param value
* the value to be associated with each of the maps keys.
* @return a map with the passed in keys initialized to value.
*/
public static IMap <K, V> create <K, V>(ICollection <K> keys, V value)
{
IMap <K, V> map = CollectionFactory.CreateInsertionOrderedMap <K, V>();
foreach (K k in keys)
{
map.Put(k, value);
}
return(map);
}
protected IMap <PropositionSymbol, bool?> initializeInferred(KnowledgeBase kb)
{
// inferred <- a table, where inferred[s] is initially false for all
// symbols
IMap <PropositionSymbol, bool?> inferred = CollectionFactory.CreateInsertionOrderedMap <PropositionSymbol, bool?>();
foreach (PropositionSymbol p in SymbolCollector.getSymbolsFrom(kb.asSentence()))
{
inferred.Put(p, false);
}
return(inferred);
}
/// <summary>
/// Handles new vertices.
/// </summary>
/// <param name="v"></param>
/// <returns></returns>
private IMap <VertexLabelType, EdgeLabelType> checkForNewVertex(VertexLabelType v)
{
IMap <VertexLabelType, EdgeLabelType> result = globalEdgeLookup.Get(v);
if (result == null)
{
result = CollectionFactory.CreateInsertionOrderedMap <VertexLabelType, EdgeLabelType>();
globalEdgeLookup.Put(v, result);
vertexLabels.Add(v);
}
return(result);
}
public void test_indexesOfValue()
{
RandVar X = new RandVar("X", new BooleanDomain());
RandVar Y = new RandVar("Y", new ArbitraryTokenDomain("A", "B", "C"));
RandVar Z = new RandVar("Z", new BooleanDomain());
// An ordered X,Y,Z enumeration of values should look like:
// 00: true, A, true
// 01: true, A, false
// 02: true, B, true
// 03: true, B, false
// 04: true, C, true
// 05: true, C, false
// 06: false, A, true
// 07: false, A, false
// 08: false, B, true
// 09: false, B, false
// 10: false, C, true
// 11: false, C, false
IRandomVariable[] vars = new IRandomVariable[] { X, Y, Z };
IMap <IRandomVariable, object> even = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, object>();
even.Put(X, true);
CollectionAssert.AreEqual(new int[] { 0, 1, 2, 3, 4, 5 },
ProbUtil.indexesOfValue(vars, 0, even));
even.Put(X, false);
CollectionAssert.AreEqual(new int[] { 6, 7, 8, 9, 10, 11 },
ProbUtil.indexesOfValue(vars, 0, even));
even.Put(Y, "A");
CollectionAssert.AreEqual(new int[] { 0, 1, 6, 7 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Y, "B");
CollectionAssert.AreEqual(new int[] { 2, 3, 8, 9 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Y, "C");
CollectionAssert.AreEqual(new int[] { 4, 5, 10, 11 },
ProbUtil.indexesOfValue(vars, 1, even));
even.Put(Z, true);
CollectionAssert.AreEqual(new int[] { 0, 2, 4, 6, 8, 10 },
ProbUtil.indexesOfValue(vars, 2, even));
even.Put(Z, false);
CollectionAssert.AreEqual(new int[] { 1, 3, 5, 7, 9, 11 },
ProbUtil.indexesOfValue(vars, 2, even));
}
public ProbabilityTable divideBy(ProbabilityTable divisor)
{
if (!randomVarInfo.GetKeys().ContainsAll(divisor.randomVarInfo.GetKeys()))
{
throw new IllegalArgumentException("Divisor must be a subset of the dividend.");
}
ProbabilityTable quotient = new ProbabilityTable(randomVarInfo.GetKeys());
if (1 == divisor.getValues().Length)
{
double d = divisor.getValues()[0];
for (int i = 0; i < quotient.getValues().Length; ++i)
{
if (0 == d)
{
quotient.getValues()[i] = 0;
}
else
{
quotient.getValues()[i] = getValues()[i] / d;
}
}
}
else
{
ISet <IRandomVariable> dividendDivisorDiff = SetOps
.difference(
CollectionFactory.CreateSet <IRandomVariable>(this.randomVarInfo.GetKeys()),
CollectionFactory.CreateSet <IRandomVariable>(divisor.randomVarInfo.GetKeys()));
IMap <IRandomVariable, RVInfo> tdiff = null;
MixedRadixNumber tdMRN = null;
if (dividendDivisorDiff.Size() > 0)
{
tdiff = CollectionFactory.CreateInsertionOrderedMap <IRandomVariable, RVInfo>();
foreach (IRandomVariable rv in dividendDivisorDiff)
{
tdiff.Put(rv, new RVInfo(rv));
}
tdMRN = new MixedRadixNumber(0, createRadixs(tdiff));
}
IMap <IRandomVariable, RVInfo> diff = tdiff;
MixedRadixNumber dMRN = tdMRN;
int[] qRVs = new int[quotient.radices.Length];
MixedRadixNumber qMRN = new MixedRadixNumber(0, quotient.radices);
ProbabilityTableIterator divisorIterator = new ProbabilityTableIteratorImp2(quotient, qRVs, qMRN, dMRN, diff, this);
divisor.iterateOverTable(divisorIterator);
}
return(quotient);
}
private void initializeHypothesisWeights(int size)
{
if (size == 0)
{
throw new RuntimeException("cannot initialize Ensemble learning with Zero Learners");
}
learnerWeights = CollectionFactory.CreateInsertionOrderedMap <ILearner, double>();
foreach (ILearner le in learners)
{
learnerWeights.Put(le, 1.0);
}
}
public IMap <Variable, Term> standardizeApart(ICollection <Literal> l1Literals,
ICollection <Literal> l2Literals,
StandardizeApartIndexical standardizeApartIndexical)
{
ISet <Variable> toRename = CollectionFactory.CreateSet <Variable>();
foreach (Literal pl in l1Literals)
{
toRename.AddAll(variableCollector.collectAllVariables(pl
.getAtomicSentence()));
}
foreach (Literal nl in l2Literals)
{
toRename.AddAll(variableCollector.collectAllVariables(nl.getAtomicSentence()));
}
IMap <Variable, Term> renameSubstitution = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
foreach (Variable var in toRename)
{
Variable v = null;
do
{
v = new Variable(standardizeApartIndexical.getPrefix()
+ standardizeApartIndexical.getNextIndex());
// Ensure the new variable name is not already
// accidentally used in the sentence
} while (toRename.Contains(v));
renameSubstitution.Put(var, v);
}
ICollection <Literal> posLits = CollectionFactory.CreateQueue <Literal>();
ICollection <Literal> negLits = CollectionFactory.CreateQueue <Literal>();
foreach (Literal pl in l1Literals)
{
posLits.Add(substVisitor.subst(renameSubstitution, pl));
}
foreach (Literal nl in l2Literals)
{
negLits.Add(substVisitor.subst(renameSubstitution, nl));
}
l1Literals.Clear();
l1Literals.AddAll(posLits);
l2Literals.Clear();
l2Literals.AddAll(negLits);
return(renameSubstitution);
}
/**
*
* @return a map for each element and the corresponding disjoint set that it
* belongs to.
*/
public IMap <E, ISet <E> > getElementToDisjointSet()
{
// Note: Instantiate normal sets to ensure IdentityHashSet
// is not exposed outside of this class.
// This also ensures the internal logic cannot
// be corrupted externally due to changing sets.
IMap <E, ISet <E> > result = CollectionFactory.CreateInsertionOrderedMap <E, ISet <E> >();
foreach (KeyValuePair <E, ISet <E> > entry in elementToSet)
{
result.Put(entry.GetKey(), CollectionFactory.CreateSet <E>(entry.GetValue()));
}
return(result);
}
public void testParanthisedSingleVariable()
{
Sentence beforeSubst = parser.parse("((( King(x))))");
Sentence expectedAfterSubst = parser.parse("King(John) ");
IMap <Variable, Term> p = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
p.Put(new Variable("x"), new Constant("John"));
Sentence afterSubst = sv.subst(p, beforeSubst);
Assert.AreEqual(expectedAfterSubst, afterSubst);
Assert.AreEqual(parser.parse("((( King(x))))"), beforeSubst);
}
public void testSubstSingleVariableFailsWithPredicate()
{
Sentence beforeSubst = parser.parse("King(x)");
Sentence expectedAfterSubst = parser.parse(" King(x) ");
IMap <Variable, Term> p = CollectionFactory.CreateInsertionOrderedMap <Variable, Term>();
p.Put(new Variable("y"), new Constant("John"));
Sentence afterSubst = sv.subst(p, beforeSubst);
Assert.AreEqual(expectedAfterSubst, afterSubst);
Assert.AreEqual(beforeSubst, parser.parse("King(x)"));
}
/// <summary>
/// Construct a Cell World with size xDimension * y Dimension cells, all with
/// their values set to a default content value.
/// </summary>
/// <param name="xDimension">the size of the x dimension.</param>
/// <param name="yDimension">the size of the y dimension.</param>
/// <param name="defaultCellContent">the default content to assign to each cell created.</param>
public CellWorld(int xDimension, int yDimension, C defaultCellContent)
{
for (int x = 1; x <= xDimension; x++)
{
IMap <int, Cell <C> > xCol = CollectionFactory.CreateInsertionOrderedMap <int, Cell <C> >();
for (int y = 1; y <= yDimension; y++)
{
Cell <C> c = new Cell <C>(x, y, defaultCellContent);
cells.Add(c);
xCol.Put(y, c);
}
cellLookup.Put(x, xCol);
}
}
